CN109459046A - The positioning and air navigation aid of suspending underwater autonomous navigation device - Google Patents
The positioning and air navigation aid of suspending underwater autonomous navigation device Download PDFInfo
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- CN109459046A CN109459046A CN201811407006.5A CN201811407006A CN109459046A CN 109459046 A CN109459046 A CN 109459046A CN 201811407006 A CN201811407006 A CN 201811407006A CN 109459046 A CN109459046 A CN 109459046A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
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Abstract
The invention discloses the positioning and air navigation aid of a kind of suspending underwater autonomous navigation device, include the following steps, S10, acquire underwater picture；S20 carries out significance analysis to acquired image；S30 carries out pretreatment and feature extraction input feature vector bag of words to acquired image；S40, progress characteristic matching, multisensor positions in real time and propeller control；The output of S20-S40 is constructed image data base by S50；S60 carries out closed loop detection according to image data base to image；S70 carries out offline optimization to image data base.The present invention is a kind of suspending underwater autonomous navigation device application environment for seabed base station, merges the positioning and air navigation aid of multi-sensor information, can effectively improve the underwater positioning accuracy of suspending underwater autonomous navigation device.
Description
Technical field
The invention belongs to submarine navigation device fields, and in particular to a kind of positioning and navigation of suspending underwater autonomous navigation device
Method.
Background technique
Suspending underwater aircraft (Hover Autonomous Underwater Vehicle, abbreviation HAUV) is a kind of
The Autonomous Underwater Vehicle for having powerful maneuverability.HAUV has multiple propellers, and have can suspend in water layer,
Have powerful up-and-down maneuver ability, can close to seabed navigate by water etc. multiple advantages.
Use cost in HAUV using traditional Underwater Navigation methods such as acoustics positioning, dead reckoning positioning is high, and deposits
Limited in the effective distance of acoustics positioning, dead reckoning has the defects of error accumulation problem.At this stage, based on sea-floor relief
Machine vision method is to solve the effective means of HAUV High precision underwater positioning and navigation problem.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of HAUV application environments for seabed base station, merge more sensings
The positioning and air navigation aid of device information can effectively improve the underwater positioning accuracy of HAUV.
In order to solve the above technical problems, the present invention adopts the following technical scheme that:
A kind of positioning and air navigation aid of suspending underwater autonomous navigation device, include the following steps,
S10 acquires underwater picture；
S20 carries out significance analysis to acquired image；
S30 carries out pretreatment and feature extraction input feature vector bag of words to acquired image；
S40, progress characteristic matching, multisensor positions in real time and propeller control；
The output of S20-S40 is constructed image data base by S50；
S60 carries out closed loop detection according to image data base to image；
S70 carries out offline optimization to image data base.
Preferably, the multisensor positions in real time, comprising the following steps:
S41, if k moment posePose variation is calculated by inertial navigation unitPass through electronics
Compass obtains azimuthal variation
S42, such asFor threshold value, it is determined that course angle variable quantity
S43, such asThen determine that electronic compass is interfered by external magnetic field
S44 carries out visual pattern matching；
S45, if it matches, then calculating pose variation by homography matrix
S46, if it does not match, being determined as error hiding, k+1 moment pose is
S47, if position deviationAnd
S48 is to be judged to correctly matching, and k+1 moment pose isOtherwise S46.
Preferably, the significance analysis is measured by comentropy.
Preferably, the characteristic matching detects characteristic point using ORB feature operator, is excluded using RANSAC algorithm invalid special
Sign.
Preferably, closed loop detection the following steps are included:
S61, from image of the distance found out in image data base with current location within setting value；
S62 compares present image and its cosine similarity one by one；
S63 carries out characteristic matching if cosine similarity is higher than setting value.
Preferably, the offline optimization uses BA nonlinear method.
Using the present invention have it is following the utility model has the advantages that
1, the present invention is higher than setting value as standard using cosine similarity by the way of closed loop detection, to determine whether into
Characteristic matching between row image.According to camera position corresponding to the homography matrix calculating present image between closed image, thus
The accumulated error of that section of voyage before elimination closed loop point.It is calculated by the relative position between closed image, it can be to postorder image
Position be corrected, to improve the position precision of image sequence.
2, present invention sensor used for positioning includes inertial navigation unit, electronic compass, altimeter and camera, is hanged
Positioning and navigation during the navigation of floating underwater autonomous navigation device use on-line Algorithm, by machine vision engagement height, are used to
Property navigation elements information, directional information calculate the current pose of suspending underwater autonomous navigation device in real time, and pass through pose and feed back
Propeller is controlled, so that robot is navigated by water with preset path.
3, off-line algorithm of the present invention in database image and location information calculated again, obtain more accurate
Map.
Detailed description of the invention
Fig. 1 is the step flow chart of the embodiment of the present invention；
Fig. 2 is that Multi-sensor Fusion positions specific steps flow chart in the embodiment of the present invention；
Fig. 3 is that closed loop detects specific steps flow chart in the embodiment of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are some of the embodiments of the present invention, instead of all the embodiments.Based on this hair
Embodiment in bright, every other implementation obtained by those of ordinary skill in the art without making creative efforts
Example, shall fall within the protection scope of the present invention.
The invention discloses the positioning and air navigation aid of a kind of suspending underwater autonomous navigation device, specific embodiment referring to
Fig. 1-3 and as described below.
S10 acquires underwater picture；
S20 carries out significance analysis to acquired image；
S30 carries out pretreatment and feature extraction input feature vector bag of words to acquired image；
S40, progress characteristic matching, multisensor positions in real time and propeller control；
The output of S20-S40 is constructed image data base by S50；
S60 carries out closed loop detection according to image data base to image；
S70 carries out offline optimization to image data base.
Multisensor positions in real time in S40, comprising the following steps:
S41, if k moment posePose variation is calculated by inertial navigation unitPass through electronics
Compass obtains azimuthal variation
S42, such asFor threshold value, it is determined that course angle variable quantity
S43, such asThen determine that electronic compass is interfered by external magnetic field
S44 carries out visual pattern matching；
S45, if it matches, then calculating pose variation by homography matrix
S46, if it does not match, being determined as error hiding, k+1 moment pose is
S47, if position deviationAnd
S48 is to be judged to correctly matching, and k+1 moment pose isOtherwise S46.
Specifically, in above-mentioned steps, first compare the data of inertial navigation unit and electronic compass, determine that course angle changes
AmountScreening foundation is that electronic compass is more sensitive for periphery electromagnetic environment, and inertial navigation unit precision is lower, and deposits
In accumulated error, but it is stable, it is seldom interfered by ambient enviroment, therefore passes through threshold valueTo determine having for electronic compass data
Effect property effectively such as it then uses the surveyed course angle of electronic compass, if it is invalid, then uses the surveyed course angle of inertial navigation unit.
Since water-bed environment is complicated and changeable, in fact it could happen that the case where images match fails uses inertial navigation unit at this time
Surveyed coordinate data and vectoring angular data update current posture information.If images match success, utilizes inertial navigation
Cell data and course angle information test to vision positioning result, if the two is not much different, it is possible to determine that are image
With correct, take the surveyed coordinate of vision positioning and vectoring angular data updates current posture information.Conversely, then determining images match
Mistake, is subject to inertial navigation unit data.
Machine vision positioning in above-mentioned steps uses images match to realize, specific formula is as follows:
In above formula, (Δ x, Δ y) are changes in coordinates of the image center in world coordinate system, and T is navigation body coordinate system
To the transition matrix of world coordinate system, it is assumed here that navigation body coordinate system coincides with picture centre coordinate system,It is current
The course of moment aircraft, K is simplified camera parameter matrix, with camera heights h (being obtained by altitude measuring) with camera x, the direction y
Pixel focal length fxAnd fyRatio reflect that imaging plane to the size conversion of seabed plane, has ignored camera imaging in matrix
Distortion, H is homography matrix of the eve image to present image, since camera is towards substantially vertical seabed plane, and phase
The height approximation of machine is constant, which can be approximated to be equilong transformation.H-matrix is solved by Feature Points Matching described below
It arrives.θ indicates the relative rotation angle of image, tx、tyIndicate the relative displacement of image, unit is pixel.The effect of P matrix be by
The coordinate origin of the plane of delineation is moved to picture centre, and W is picture traverse, and H is picture altitude, and unit is pixel.
Since the sensor in above-mentioned steps has measurement error, institute's measured data is filtered using EKF algorithm.For
Raising operation efficiency simplifies state conversion, and EKF is with the two dimension of camera between the two images that are obtained by fuse information location algorithm
(current course angle of Δ x, Δ y) and HAUV change changes in coordinatesState vector is formed, i.e.,
Its state equation is xk=f (xk-1,υk-1)+wk-1
In formula, f () indicates the kinetic model of system, has reacted system input υk-1Influence to system state change,
wk-1It is the process noise of Normal Distribution.Input parameter of the invention is the revolving speed of two propellers, is measured by experiment,
Revolution speed of propeller can be obtained roughly for the influence function of suspending underwater autonomous navigation device forward speed and turning velocity, from
And the foundation of completion status equation.
Measuring equation is zk=h (xk)+vk
In formula, vkIt is the observation noise of Normal Distribution；H () indicate state vector to observation vector transfer function,
The actually reaction of the variation of camera position on a sensor.
After the processing of EKF, using starting point as origin, the current pose of image center can be expressed as
Since electronic compass can measure absolute course angleThe accumulation of error may be not present, therefore, if electronic compass data are normal, take
It, which reads, is used as current course angle, and current pose is represented by
Significance analysis is measured by comentropy in S20, and characteristic matching detects feature using ORB feature operator in S40
Point excludes invalid feature using RANSAC algorithm, specifically,
Characteristic point is detected using ORB feature operator, invalid feature is excluded with RANSAC algorithm, passes through feature between two images
The mapping of point solves homography matrix.
The quality of image is measured with conspicuousness.Fuzzy processing is carried out to picture first.This is tiny and numerous in order to exclude
More characteristic points only retains biggish feature.Saliency is measured by comentropy E, and calculation formula is as follows:
Wherein, wk indicates a feature vocabulary in image, it is assumed that its sum is n, and p (wk) is that wk vocabulary goes out in the picture
Existing frequency.From the formula it can be seen that feature vocabulary type is more, distribution is more uniform, and comentropy is bigger, can more embody
The conspicuousness of image.
In S60 closed loop detection the following steps are included:
S61, from image of the distance found out in image data base with current location within setting value；
S62 compares present image and its cosine similarity one by one；
S63 carries out characteristic matching if cosine similarity is higher than setting value.
Specifically, during map is explored, the quantity of vocabulary is ever-increasing in feature lexicon.Use Feature Words
Remittance histogram vectors (nw1,nw2...nwk) characterize image (nwkIndicate feature vocabulary wkThe number occurred in the picture), it is assumed that
At a time, the feature histogram vector of the i-th width image is xi, the feature vocabulary histogram vectors of jth width image are xj, then
The similarity degree of two images can be indicated with cosine similarity:
In closed loop detection process, the first image from the distance found out in database with current location within setting value, by
One compares cosine similarity of the present image with them, if cosine similarity is higher than setting value, carries out characteristic matching.If
Characteristic matching is successful, then the camera position according to corresponding to the homography matrix calculating present image between closed image, to eliminate
The accumulated error of that section of voyage before between closed loop point.If being more than piece image successful match, the higher figure of conspicuousness is chosen
As carrying out dead reckoning.
In S50, in order to effectively manage image, the analysis of image is stored as a result, and convenient for the utilization again of image, foundation
Image data base manages image.Database synchronous building during HAUV is navigated by water, wherein the quasi- data item included is as follows,
Offline optimization in S70, since the operational capability of robot kernel is limited, on-line Algorithm is by sacrificing certain efficiency
Improve execution efficiency.And after HAUV completes navigational duty, there is time enough to carry out global optimization offline, obtains higher precision
Map, so as to next time navigation in use, therefore the target of offline optimization be disregard calculate cost obtain high-precision map (when
So, also to accomplish that calculation amount is as small as possible under equal accuracy).The present invention is using the nonlinear optimization side BA being extensively examined
Method.Since sea-floor relief is approximately plane, and camera moves in the planes, can simplify traditional BA problem with the two constraints,
Construct following cost function:
In formula,WithThe i-th width image and jth width image are respectively indicated to public map plane (for splicing
Have the imaginary plane of image) homography matrix,WithIt is the match point on the i-th width image and jth width image respectively.Optimization
Target is so that distance is minimum between projection of all match points on map plane.It is asked with column Wen Baige-Ma Kuaer special formula method
Above-mentioned least square problem, homography matrix between available optimal terrain piecing figure and image-map are solved, and then is optimized
Homography matrix between image-image afterwards.Homography matrix is substituted into formula:
Camera position corresponding to image can be acquired, to complete the global nonlinear optimization of image data.
It should be appreciated that exemplary embodiment as described herein is illustrative and be not restrictive.Although being retouched in conjunction with attached drawing
One or more embodiments of the invention is stated, it should be understood by one skilled in the art that not departing from through appended right
In the case where the spirit and scope of the present invention defined by it is required that, the change of various forms and details can be made.
Claims (6)
1. a kind of positioning and air navigation aid of suspending underwater autonomous navigation device, which comprises the following steps:
S10 acquires underwater picture；
S20 carries out significance analysis to acquired image；
S30 carries out pretreatment and feature extraction input feature vector bag of words to acquired image；
S40, progress characteristic matching, multisensor positions in real time and propeller control；
The output of S20-S40 is constructed image data base by S50；
S60 carries out closed loop detection according to image data base to image；
S70 carries out offline optimization to image data base.
2. the positioning and air navigation aid of suspending underwater autonomous navigation device as described in claim 1, which is characterized in that described more
Sensor positions in real time, comprising the following steps:
S41, if k moment pose x, y,Pose changes delta x is calculated by inertial navigation uniti,Δyi,Pass through electronics sieve
It examines and seizes and takes azimuthal variation
S42, such as For threshold value, it is determined that course angle variable quantity
S43, such asThen determine that electronic compass is interfered by external magnetic field
S44 carries out visual pattern matching；
S45, if it matches, then calculating pose changes delta x by homography matrixv,Δyv,
S46, if it does not match, being determined as error hiding, k+1 moment pose is x+ Δ xi,y+Δyi,
S47, if position deviationAnd
S48 is to be judged to correctly matching, and k+1 moment pose is x+ Δ xv,y+Δyv,Otherwise S46.
3. the positioning and air navigation aid of suspending underwater autonomous navigation device as described in claim 1, which is characterized in that described aobvious
The analysis of work property is measured by comentropy.
4. the positioning and air navigation aid of suspending underwater autonomous navigation device as described in claim 1, which is characterized in that the spy
Sign matching detects characteristic point using ORB feature operator, excludes invalid feature using RANSAC algorithm.
5. the positioning and air navigation aid of suspending underwater autonomous navigation device as described in claim 1, which is characterized in that described to close
Ring detection the following steps are included:
S61, from image of the distance found out in image data base with current location within setting value；
S62 compares present image and its cosine similarity one by one；
S63 carries out characteristic matching if cosine similarity is higher than setting value.
6. the positioning and air navigation aid of suspending underwater autonomous navigation device as described in claim 1, which is characterized in that it is described from
Line optimization uses BA nonlinear method.
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